I've been working as the Social Media Editor and a staff writer at Forbes since October 2011. Prior to that, I worked as a freelance writer and contributor here. On this blog, I focus on futurism, cutting edge technology, and breaking research. Follow me on Twitter - @thealexknapp. You can email me at aknapp@forbes.com

Dr. Bradley Voytek is a neuroscientist, is currently a Post-Doctoral Fellow at UCSF working with Adam Gazzaley and is Data Evangelist for Uber, Inc. On the internet, he’s probably most famous for one of the most neglected branches of research in neuroscience – the zombie brain. However, he’s also engaged in a number of exciting research projects, including how brain lesions affect stroke victims, using data mining to develop neuroscience hypotheses, and engaging with the outside world through his blog and his Twitter account. I had a chance to have a wide ranging conversation with Voytek, covering neuroscience, zombies, and what academia can learn from Silicon Valley.

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Alex: Tell me a little about your background.

Voytek: I started undergrad studies a year early – I went to USC and initially I was a physics major for two years, and I was interested in astrophysics. Due to various life circumstances I wasn’t doing well at it. I was living with my grandfather, and he was diagnosed with Parkinson’s disease. He used to be an engineer at General Dynamics. Growing up with him got me interested in a big part of my technical interests. Watching him devolve with Parkinson’s Disease got me interested in neuroscience. At the time, USC didn’t have a neuroscience major, so I switched to Psychology because it was close and I could finish on time. In addition to the psychology courses, I also took classes in programming, artificial intelligence, and Philosophy of Mind to round it out.

As an undergrad, I joined a lab, and the professor wanted me to take a bunch of data stored as .txt files and open them and copy and paste them into Excel. He gave me three weeks to finish. Instead, I wrote a program to crank out the data in an afternoon, came to him the next day. He was blown away. I was blown away that he was blown away. That started my path to how I can combine technical skills with neurosciences to utilize computers to make data connections. For example, average fMRI’s end up throwing out interesting data that isn’t relevant to the study. But what can we learn from that data? How can we combine data to discover new things?

What’s the focus of your work now?

When I started work on my PhD, I went to UC Berkeley. I was interested in how different brain areas communicate to give rise to cognition. Particularly, how does the brain balance learning – which is dependent on malleability flexibility – with a stable sense of self and cognition? So I ended up doing research with Dr. Robert Knight, who basically made his career by working with patients with brain lesions. What we were interested in is how circumscribed lesions to one brain area affect behavior? And can we see that effect using an EEG?

In the neurosciences, there’s what we call “cortical chauvinism” – that is, the neocortex gets the most research. But in our recent published research, we looked at patients with large damage to prefrontal cortex, as well as patients who just had small damage to the striatum. The striatum is very interconnected with lots of different places of the brain. So we hypothesized that for this network node, even a small lesion would do more damage than a larger lesion in the prefrontal corext. Basically, you can compare it to losing just the internet at your house when your modem dies versus your whole neighborhood losing internet when your service provider crashes. One causes network-wide damage, while the other is just limited to one place.

So where has that research taken you now?

Well, in the process of doing this, I wanted to know what regions connected to the striatum, and where do those connections go? I ended up doing months and months of research. It was ridiculous – had to read tons and tons of papers. What I wanted to know was, why can’t I have a website where I type brain region, and just visually see where research shows that region connects to?

So my wife and I were sitting at home one night, and realized that I figured out how I can do this. I gave her the basic idea, and her response was simple: “I can totally code that faster than you.” So she and I got into a coding challenge – she won. So she’s done the basic programming for what we’re calling brainSCANr. Here’s the basic idea: we have the entire purview of public literature in medicine and neuroscience – about 20 million peer reviewed papers. There’s a lot of information in there. In that information, I believe that a lot of basic facts of how the brain works are known. The problem is synthesis! Ion channels, animal studies of behavior, human studies of cognition, spiking probabilities, and so on.

So what we did is configure a database to look for key words connecting to concepts. For example, a search for “learning and memory” yields 42,000 papers! So it’s probably safe to say that they’re connected. By contrast, “learning and coma” yields only about 200 papers, so they’re probably less connected. So we basically created a dictionary of neuroscience terms: brain regions, neurotransmitter names, cognition, then determined how those 700 terms relate to the other 700 terms in PubMed based on that type of probability of association. When you do that – it turns out there’s actually a lot of structure in the literature. So this is actually a surrogate for knowledge – text mining peer-reviewed publications to learn more about the brain.

Of course, there’s a lot of biases in peer review. Positive publication bias will skew connections, since text mining doesn’t differentiate between, for example, “learning is related to memory” from “learning is not related to memory.”

What’s interesting, though, is that I plugged my tool into Paul Allen’s Atlas project, which shows how genes express in different parts of the brain. What I discovered is that the brain areas for serotonin heavily studied in the literature and areas where it’s highly expressed aren’t always the same! This exposes a publication bias because different brain regions are harder to work with than others.

What’s been frustrating is that we did all this work in about eleven days, but it’s been in peer review for ten months. Several journals have rejected our work because they didn’t see the application

Kind of seems like the bias towards specialization in science, right? I mean, to me the application is obvious! Let’s take the data we have and see what else we can learn from it.

Well, there was one piece of advice I got when I was looking into my postdoc research – “you need to figure out what your story is: what is your schtick when you go on the job market? That way people will understand what it is you study.” But I’m more interested in bringing together disparate data and seeing where it goes. So I’m kind of hedging my bets doing work with tech companies. I find that the tech community appreciates people who understand data and know what to do with it.

With that in mind, last summer I did what I call my “Startup Sabbatical” at Uber – an on-demand car service. Basically, it works by using your phone to request a car and it comes to pick you up based on your GPS location. I took the opportunity because I wanted to learn new techniques and skills. It was a good fit for me because in academia there’s a “domain focus” – for a vast majority of researchers, you have your domain and that’s what you stick to. In Silicon Valley, it’s very different. Startups pivot all the time – they start as one company, and if it doesn’t work, they become another kind of company. There’s a lot of highly skilled engineers who aren’t going into academia because they want more flexibility in what they do.

So one thing I was interested in while I was working for them was geolocation based analytics. I was focused on demand prediction – how can we place cars where people will want them so that we can minimize pickup times? Uber’s goal from call to car was five minutes. That meant having a good idea of figuring out where people will want to hang out in San Francisco. I figured that people used the service for when they’re bar hopping and stuff like that. So what we needed was “party density” – where are people most likely to party a lot and need a ride home? So we combined their geolocation pickup data – where they picked people up from most often, and correlated that with crime data – on the theory that the more people were in a given place, the more reports of crime there would be. And it worked!

From some of the connections I made in Silicon Valley, I’ve been trying to get people with “big data” skills interested in solving the problems I want to work on in the neurosciences.

I’ve noticed from your blog that you seem to want to take part of this ethos into the traditional peer review models.

Absolutely – we shouldn’t get rid of peer review. It’s valuable, but it’s very slow. Science is moving too quickly now, and one thing we know is that there’s more than one way to analyze one dataset. But that’s not what we do with it. Take the work I’m doing with neurosurgeons. They put electrodes on the surface of the brain to treat epilepsy and we use those data to also get clearer images of what’s going on, say, in terms of working memory. But the data we have is rare data there aren’t a lot of people getting the kind of surgery needed to collect this data. If all we do is just use that data, analyze one task, make a scatter plot and call it a day, we end up tossing out a ton of data points. It’s like that in every lab. There’s tons of data that sits on some hard drive for awhile. If the lab is tech savvy they’ll have a backup system and archive. Otherwise, the data will disappear. That’s incredibly wasteful.

But I think that will change as more tech savvy people get their own labs. We’ll start to see more archiving and more data sharing. It’s inevitable. I’m trying to do my small part by being open when I can.

So, I’m talking to you and it’s October, so I’d be remiss if I didn’t ask about your groundbreaking zombie research. So why focus on zombies?

Comparison of human vs. zombie brains.

It all started when I gave a talk at TEDx Berkeley last year about the research I’m doing on patients with strokes. My long term goal here is that I want to apply what I’m learning to health issues. It’s a big gamble, right? I could go to medical school and definitely save lives. But as a scientist, I may never help anyone, but I have the chance to help millions. That’s one reason why I focus on neuroscience.

The other reason is the first thing I mentioned in my talk: I’m a big geek. I grew up reading comics and sci fi and playing video games. I saw the power of technology in science fiction, and realized it doesn’t all have to be fiction. So I’m trying to help people and I also have a desire to see cool things happen. So last summer, after the talk, I got a phone call out of the blue from head of Matt Mogk, the head of the Zombie Research Society. He asked if I were into zombies, because he would love to have some neuroscientists talk about zombie brain.

So my friend and fellow neuroscientist Timothy Verstynen and I started our “research”. Tim rendered 3-D zombie brains and we developed our theory of ‘consciousness deficit hypoactivity disorder.’ It’s been fun doing public lectures. I’m a big science outreach advocate, and so I was excited to do this. It’s also a chance to hit the ridiculous reductionism that some studies do with imaging. Like fMRI studies that try to say “here’s where love is.” They way I see it, if I can make a plausible argument about how to make a zombie from brain scans, then that shows how easy it is to create “just so” stories in neuroscience.

Recently, Tim got an email from grad student at UCSD. He was teaching his course, and he overheard undergrads arguing about zombie brain. He said that he had never heard undergrads talk about neuroscience more passionately. That’s totally why I do this.

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Comments

Great to see Bradley being interviewed here, he’s a great guy and his interactions on Quora are always cool. And having a neuroscientist discuss zombies is always great — we need to make sure Brad has a good survival plan for the zombie apocalypse, because we’ll need him around afterward.